Call for reviews on global health challenges
Bibliographic record
Abstract
It is our great pleasure to publish this special issue with reviews relevant to global health. IJPH launched this Call to concert the 9th European Congress on Tropical Medicine and International Health, which runs under the title ‘‘Driving the Best Science to Meet Global Health Challenges’’. The Call received high attention with 18 manuscripts submitted from 11 countries in the Americas, Europe and Asia. Although half of the submitted reviews addressed problems in low-income or developing countries, no authors from Africa responded to the Call. Does this reflect the continued inequity in the availability of research resources for global health? To manage the rigorous peer review process, we were again largely dependent on our highly committed international reviewers, greatly acknowledged in Table 1. Given the topic of the Call, we tried our best to involve experts from across the world as peer reviewers and received reports from 37 experts from 16 countries, though the majority of peer reviewers is based in USA or Canada (13), or in Europe (12). Nine reviews were finally accepted for publication. The special issue now presents an interesting range of reviews, as the contributing authors bring to our focus infectious diseases, maternal and child health, and finally physical and mental health. Repeating the major success of the IJPH Call launched in 2013 (Kunzli 2013), we have again asked the audience of the conference to choose the most interesting review based on a set of four abstracts that we considered to represent the most relevant reviews that were accepted for publication. The four publications reviewed topics on equity in maternal health care service utilization, knowledge transfer strategies to improve public health in lowincome countries, injury and rehabilitation interventions in humanitarian crises and concerns on vaccination use in lowand middle-income countries. Reducing maternal mortality is one of the Millennium Development Goals (MDGs) and in the post-2015 era, it will remain an important agenda to be pursued amidst the achievement of the Sustainable Development Goals (SDGs). A systematic review by Caliskan et al. (2015) found a lack of equity in the utilization of maternal health care in developing countries, and directed our attention to not only improving maternal care but more importantly, ensuring that even the most disadvantaged mothers have equal access to the improved care. This is an important This editorial is part of the special issue ‘‘Driving the Best Science to Meet Global Health Challenges’’ edited on the occasion of the 9th European Congress on Tropical Medicine and International Health 2015.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.089 | 0.264 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.041 | 0.029 |
| Insufficient payload (model declined to judge) | 0.103 | 0.083 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".